{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b63f80e",
   "metadata": {},
   "source": [
    "# Домашнее задание №4\n",
    "\n",
    "Дан датасет игроков на баскетбольном поле. Есть файл с разметкой в формате json, в котором каждый игрок на поле обведен с помощью bbox. А также определена принадлежность каждого игрока к одной из двух команд.\n",
    "\n",
    "Что требуется сделать:\n",
    "- классифицировать игроков с помощью вектора признаков игрока (features) средний цвет в пространстве RGB;\n",
    "- классифицировать игроков с помощью вектора признаков игрока (features) средний цвет в пространстве HSV;\n",
    "- классифицировать игроков с помощью вектора признаков игрока (features) гистограмма в пространстве RGB;\n",
    "- классифицировать игроков с помощью вектора признаков игрока (features) гистограмма в пространстве HSV;\n",
    "\n",
    "Признаки формируются из области изображения, покрытой bounding box этого игрока. Число бинов определить самостоятельно.\n",
    "\n",
    "В качестве классификатора можете использовать (любой на выбор):\n",
    "- Методы без обучения (k-means или другие методы кластеризации).\n",
    "- Методы с обучением (K-Nearest Neighbors, RandomForest, GaussianNB или др.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6e021e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "import json\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cluster import KMeans, SpectralClustering\n",
    "import optuna\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from sport_team_lib import TeamDataframeMaker, TeamClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e334be70",
   "metadata": {},
   "source": [
    "## 1 Подготовка данных для обучения\n",
    "\n",
    "### 1.1 Формирование датафрейма с признаками для обучения\n",
    "\n",
    "Для домашней работы был реализован класс TeamDataframeMaker, который позволяет получить датафрейм со всеми требуемыми данными для обучения. Документацию можно посмотреть в файле **sport_team_lib.py**.\n",
    "\n",
    "В датафрейме будут представлены следующие признаки:\n",
    "- bbox_x1, bbox_y1, bbox_x2, bbox_y2 (денормализованные координаты бокса)\n",
    "- HSV_mean_h, HSV_mean_s, HSV_mean_v (средние значения по каналам HSV для каждого бокса)\n",
    "- RGB_mean_r, RGB_mean_g, RGB_mean_b (средние значения по каналам RGB для каждого бокса)\n",
    "- HSV_hist_КОДКАНАЛА_НОМЕРБИНА (значения гистограммы бокса для каждого канала HSV)\n",
    "- RGB_hist_КОДКАНАЛА_НОМЕРБИНА (значения гистограммы бокса для каждого канала RGB)\n",
    "\n",
    "Дальше обучения модели можно будет оставлять только те признаки, которые требуются.\n",
    "\n",
    "Запустим формирование датафрейма:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "857f7b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 42\n",
    "bboxes_json = 'team_classification_data/bboxes.json'\n",
    "image_path = 'team_classification_data/frames'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c1b2e66",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_maker = TeamDataframeMaker(bboxes_json=bboxes_json, image_path=image_path)\n",
    "dataframe_maker.prepare_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd50a6f6",
   "metadata": {},
   "source": [
    "Выведем на печать случайную фотографию с боксами, чтобы убедиться в корректности денормализации координат боксов.\n",
    "Члены разных команд будут иметь боксы разных цветов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b676e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe_maker.print_random_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028b95cc",
   "metadata": {},
   "source": [
    "Выведем значения датафрейма полностью:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9a540ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>photo_number</th>\n",
       "      <th>player_number</th>\n",
       "      <th>target</th>\n",
       "      <th>photo</th>\n",
       "      <th>bbox_x1</th>\n",
       "      <th>bbox_y1</th>\n",
       "      <th>bbox_x2</th>\n",
       "      <th>bbox_y2</th>\n",
       "      <th>HSV_mean_h</th>\n",
       "      <th>HSV_mean_s</th>\n",
       "      <th>...</th>\n",
       "      <th>HSV_hist_v_tbin_6</th>\n",
       "      <th>HSV_hist_v_tbin_7</th>\n",
       "      <th>HSV_hist_v_tbin_8</th>\n",
       "      <th>HSV_hist_v_tbin_9</th>\n",
       "      <th>HSV_hist_v_tbin_10</th>\n",
       "      <th>HSV_hist_v_tbin_11</th>\n",
       "      <th>HSV_hist_v_tbin_12</th>\n",
       "      <th>HSV_hist_v_tbin_13</th>\n",
       "      <th>HSV_hist_v_tbin_14</th>\n",
       "      <th>HSV_hist_v_tbin_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>team_classification_data/frames\\1310.jpeg</td>\n",
       "      <td>147.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>54.624024</td>\n",
       "      <td>99.345608</td>\n",
       "      <td>...</td>\n",
       "      <td>382.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>3894.0</td>\n",
       "      <td>1352.0</td>\n",
       "      <td>736.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1310</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>team_classification_data/frames\\1310.jpeg</td>\n",
       "      <td>871.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>48.311395</td>\n",
       "      <td>119.726326</td>\n",
       "      <td>...</td>\n",
       "      <td>1334.0</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>2037.0</td>\n",
       "      <td>4366.0</td>\n",
       "      <td>5771.0</td>\n",
       "      <td>5131.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>2682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1310</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>team_classification_data/frames\\1310.jpeg</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>68.614780</td>\n",
       "      <td>94.580313</td>\n",
       "      <td>...</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>1177.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>1121.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>1554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1310</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>team_classification_data/frames\\1310.jpeg</td>\n",
       "      <td>621.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>58.464427</td>\n",
       "      <td>116.898325</td>\n",
       "      <td>...</td>\n",
       "      <td>2391.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1580.0</td>\n",
       "      <td>3391.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1310</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>team_classification_data/frames\\1310.jpeg</td>\n",
       "      <td>668.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>51.483625</td>\n",
       "      <td>113.362187</td>\n",
       "      <td>...</td>\n",
       "      <td>897.0</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>803.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>2260.0</td>\n",
       "      <td>2917.0</td>\n",
       "      <td>5322.0</td>\n",
       "      <td>3910.0</td>\n",
       "      <td>2998.0</td>\n",
       "      <td>6865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>170704</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>team_classification_data/frames\\170704.jpeg</td>\n",
       "      <td>384.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>49.162621</td>\n",
       "      <td>95.546845</td>\n",
       "      <td>...</td>\n",
       "      <td>412.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1712.0</td>\n",
       "      <td>3708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>170704</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>team_classification_data/frames\\170704.jpeg</td>\n",
       "      <td>612.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>46.839616</td>\n",
       "      <td>113.448266</td>\n",
       "      <td>...</td>\n",
       "      <td>993.0</td>\n",
       "      <td>1628.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>4096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>170704</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>team_classification_data/frames\\170704.jpeg</td>\n",
       "      <td>681.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>55.340410</td>\n",
       "      <td>113.653291</td>\n",
       "      <td>...</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>1372.0</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>2462.0</td>\n",
       "      <td>4380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>170704</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>team_classification_data/frames\\170704.jpeg</td>\n",
       "      <td>373.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>53.137027</td>\n",
       "      <td>92.904067</td>\n",
       "      <td>...</td>\n",
       "      <td>282.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>865.0</td>\n",
       "      <td>1748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>170704</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>team_classification_data/frames\\170704.jpeg</td>\n",
       "      <td>817.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>77.587639</td>\n",
       "      <td>82.887285</td>\n",
       "      <td>...</td>\n",
       "      <td>624.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>1282.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     photo_number  player_number  target  \\\n",
       "0            1310              0       0   \n",
       "1            1310              1       0   \n",
       "2            1310              2       0   \n",
       "3            1310              3       1   \n",
       "4            1310              4       0   \n",
       "..            ...            ...     ...   \n",
       "995        170704              5       0   \n",
       "996        170704              6       0   \n",
       "997        170704              7       1   \n",
       "998        170704              8       1   \n",
       "999        170704              9       1   \n",
       "\n",
       "                                           photo  bbox_x1  bbox_y1  bbox_x2  \\\n",
       "0      team_classification_data/frames\\1310.jpeg    147.0    255.0    228.0   \n",
       "1      team_classification_data/frames\\1310.jpeg    871.0    313.0   1001.0   \n",
       "2      team_classification_data/frames\\1310.jpeg   1126.0    244.0   1210.0   \n",
       "3      team_classification_data/frames\\1310.jpeg    621.0    276.0    712.0   \n",
       "4      team_classification_data/frames\\1310.jpeg    668.0    356.0    801.0   \n",
       "..                                           ...      ...      ...      ...   \n",
       "995  team_classification_data/frames\\170704.jpeg    384.0    266.0    486.0   \n",
       "996  team_classification_data/frames\\170704.jpeg    612.0    320.0    712.0   \n",
       "997  team_classification_data/frames\\170704.jpeg    681.0    333.0    787.0   \n",
       "998  team_classification_data/frames\\170704.jpeg    373.0    267.0    434.0   \n",
       "999  team_classification_data/frames\\170704.jpeg    817.0    202.0    872.0   \n",
       "\n",
       "     bbox_y2  HSV_mean_h  HSV_mean_s  ...  HSV_hist_v_tbin_6  \\\n",
       "0      407.0   54.624024   99.345608  ...              382.0   \n",
       "1      534.0   48.311395  119.726326  ...             1334.0   \n",
       "2      442.0   68.614780   94.580313  ...             2156.0   \n",
       "3      484.0   58.464427  116.898325  ...             2391.0   \n",
       "4      597.0   51.483625  113.362187  ...              897.0   \n",
       "..       ...         ...         ...  ...                ...   \n",
       "995    385.0   49.162621   95.546845  ...              412.0   \n",
       "996    490.0   46.839616  113.448266  ...              993.0   \n",
       "997    516.0   55.340410  113.653291  ...             1150.0   \n",
       "998    381.0   53.137027   92.904067  ...              282.0   \n",
       "999    342.0   77.587639   82.887285  ...              624.0   \n",
       "\n",
       "     HSV_hist_v_tbin_7  HSV_hist_v_tbin_8  HSV_hist_v_tbin_9  \\\n",
       "0                261.0              329.0              439.0   \n",
       "1               1927.0             1297.0             1036.0   \n",
       "2               1086.0             1050.0              986.0   \n",
       "3               1114.0              747.0              700.0   \n",
       "4               1369.0              803.0             1160.0   \n",
       "..                 ...                ...                ...   \n",
       "995              312.0              268.0              222.0   \n",
       "996             1628.0              602.0              411.0   \n",
       "997             1085.0              655.0              640.0   \n",
       "998              213.0              223.0              173.0   \n",
       "999              495.0              531.0              449.0   \n",
       "\n",
       "     HSV_hist_v_tbin_10  HSV_hist_v_tbin_11  HSV_hist_v_tbin_12  \\\n",
       "0                 436.0               391.0              1088.0   \n",
       "1                2037.0              4366.0              5771.0   \n",
       "2                1177.0              1401.0              1121.0   \n",
       "3                1580.0              3391.0              1991.0   \n",
       "4                2260.0              2917.0              5322.0   \n",
       "..                  ...                 ...                 ...   \n",
       "995               304.0               390.0               644.0   \n",
       "996               436.0               596.0              1303.0   \n",
       "997               686.0               794.0              1372.0   \n",
       "998               216.0               261.0               299.0   \n",
       "999               445.0               533.0               428.0   \n",
       "\n",
       "     HSV_hist_v_tbin_13  HSV_hist_v_tbin_14  HSV_hist_v_tbin_15  \n",
       "0                3894.0              1352.0               736.0  \n",
       "1                5131.0              1563.0              2682.0  \n",
       "2                 673.0               512.0              1554.0  \n",
       "3                 628.0               366.0              1126.0  \n",
       "4                3910.0              2998.0              6865.0  \n",
       "..                  ...                 ...                 ...  \n",
       "995              1021.0              1712.0              3708.0  \n",
       "996              2468.0              1794.0              4096.0  \n",
       "997              1819.0              2462.0              4380.0  \n",
       "998               345.0               865.0              1748.0  \n",
       "999               340.0               324.0              1282.0  \n",
       "\n",
       "[1000 rows x 110 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_maker.df_sport_teams_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d29ff3",
   "metadata": {},
   "source": [
    "### 1.2 Проверка базовых статистик для типов признаков\n",
    "\n",
    "Проверим базовые статистики по каждому виду признаков для каждой команды.\n",
    "\n",
    "Здесь нужно понять есть ли различия в базовых статистиках признаков для разных команд, если различий нет, то такие признаки лучше удалить."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffb67efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Датафрейм для первой команды\n",
    "df_team_1 = dataframe_maker.df_sport_teams_[dataframe_maker.df_sport_teams_.target == 0]\n",
    "# Датафрейм для второй команды\n",
    "df_team_2 = dataframe_maker.df_sport_teams_[dataframe_maker.df_sport_teams_.target == 1]\n",
    "\n",
    "df_teams_list = [df_team_1, df_team_2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1367f47",
   "metadata": {},
   "source": [
    "#### 1.2.1 Проверка типа RGB_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "658883f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Базовые статистики типа RGB_mean для команды 0\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RGB_mean_r</th>\n",
       "      <th>RGB_mean_g</th>\n",
       "      <th>RGB_mean_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>155.495667</td>\n",
       "      <td>130.856073</td>\n",
       "      <td>112.375949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.578723</td>\n",
       "      <td>25.779500</td>\n",
       "      <td>16.655831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>83.052047</td>\n",
       "      <td>68.159649</td>\n",
       "      <td>64.873667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>134.451448</td>\n",
       "      <td>112.358946</td>\n",
       "      <td>100.843902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>153.574477</td>\n",
       "      <td>127.980705</td>\n",
       "      <td>112.248648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>174.849200</td>\n",
       "      <td>146.752857</td>\n",
       "      <td>122.571619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>240.949526</td>\n",
       "      <td>210.759836</td>\n",
       "      <td>158.976798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RGB_mean_r  RGB_mean_g  RGB_mean_b\n",
       "count  502.000000  502.000000  502.000000\n",
       "mean   155.495667  130.856073  112.375949\n",
       "std     29.578723   25.779500   16.655831\n",
       "min     83.052047   68.159649   64.873667\n",
       "25%    134.451448  112.358946  100.843902\n",
       "50%    153.574477  127.980705  112.248648\n",
       "75%    174.849200  146.752857  122.571619\n",
       "max    240.949526  210.759836  158.976798"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Базовые статистики типа RGB_mean для команды 1\n"
     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RGB_mean_r</th>\n",
       "      <th>RGB_mean_g</th>\n",
       "      <th>RGB_mean_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>128.796935</td>\n",
       "      <td>107.778787</td>\n",
       "      <td>96.066988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.166479</td>\n",
       "      <td>23.981695</td>\n",
       "      <td>16.107621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60.539333</td>\n",
       "      <td>55.436833</td>\n",
       "      <td>57.065046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>107.933279</td>\n",
       "      <td>89.717553</td>\n",
       "      <td>85.358012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>127.814410</td>\n",
       "      <td>105.545556</td>\n",
       "      <td>95.213956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>147.386317</td>\n",
       "      <td>124.295335</td>\n",
       "      <td>106.203107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>224.235346</td>\n",
       "      <td>197.520048</td>\n",
       "      <td>153.911188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RGB_mean_r  RGB_mean_g  RGB_mean_b\n",
       "count  498.000000  498.000000  498.000000\n",
       "mean   128.796935  107.778787   96.066988\n",
       "std     28.166479   23.981695   16.107621\n",
       "min     60.539333   55.436833   57.065046\n",
       "25%    107.933279   89.717553   85.358012\n",
       "50%    127.814410  105.545556   95.213956\n",
       "75%    147.386317  124.295335  106.203107\n",
       "max    224.235346  197.520048  153.911188"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_type = 'RGB_mean'\n",
    "for idx, df_team in enumerate(df_teams_list):\n",
    "    print(f\"Базовые статистики типа {col_type} для команды {idx}\")\n",
    "    display(df_team.loc[:, [col_type in colname for colname in dataframe_maker.df_sport_teams_.columns]].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f952963c",
   "metadata": {},
   "source": [
    "#### 1.2.2 Проверка типа HSV_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "32c22983",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Базовые статистики типа HSV_mean для команды 0\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HSV_mean_h</th>\n",
       "      <th>HSV_mean_s</th>\n",
       "      <th>HSV_mean_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.201962</td>\n",
       "      <td>101.874940</td>\n",
       "      <td>162.430893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.249614</td>\n",
       "      <td>12.829122</td>\n",
       "      <td>27.447630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.359664</td>\n",
       "      <td>69.265353</td>\n",
       "      <td>91.071637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.073746</td>\n",
       "      <td>92.658787</td>\n",
       "      <td>142.975206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.874905</td>\n",
       "      <td>101.927144</td>\n",
       "      <td>160.125472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.875072</td>\n",
       "      <td>111.000026</td>\n",
       "      <td>181.590829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112.157728</td>\n",
       "      <td>135.236888</td>\n",
       "      <td>240.971271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HSV_mean_h  HSV_mean_s  HSV_mean_v\n",
       "count  502.000000  502.000000  502.000000\n",
       "mean    57.201962  101.874940  162.430893\n",
       "std     19.249614   12.829122   27.447630\n",
       "min     20.359664   69.265353   91.071637\n",
       "25%     44.073746   92.658787  142.975206\n",
       "50%     55.874905  101.927144  160.125472\n",
       "75%     69.875072  111.000026  181.590829\n",
       "max    112.157728  135.236888  240.971271"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Базовые статистики типа HSV_mean для команды 1\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HSV_mean_h</th>\n",
       "      <th>HSV_mean_s</th>\n",
       "      <th>HSV_mean_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.421444</td>\n",
       "      <td>103.402858</td>\n",
       "      <td>136.500479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.908091</td>\n",
       "      <td>14.276161</td>\n",
       "      <td>26.664636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.484472</td>\n",
       "      <td>46.950459</td>\n",
       "      <td>62.271500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>52.035975</td>\n",
       "      <td>94.974601</td>\n",
       "      <td>117.590840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64.079305</td>\n",
       "      <td>105.652270</td>\n",
       "      <td>135.789635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>82.048734</td>\n",
       "      <td>113.452266</td>\n",
       "      <td>154.687873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>123.348061</td>\n",
       "      <td>134.162491</td>\n",
       "      <td>224.289251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HSV_mean_h  HSV_mean_s  HSV_mean_v\n",
       "count  498.000000  498.000000  498.000000\n",
       "mean    66.421444  103.402858  136.500479\n",
       "std     21.908091   14.276161   26.664636\n",
       "min     19.484472   46.950459   62.271500\n",
       "25%     52.035975   94.974601  117.590840\n",
       "50%     64.079305  105.652270  135.789635\n",
       "75%     82.048734  113.452266  154.687873\n",
       "max    123.348061  134.162491  224.289251"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_type = 'HSV_mean'\n",
    "for idx, df_team in enumerate(df_teams_list):\n",
    "    print(f\"Базовые статистики типа {col_type} для команды {idx}\")\n",
    "    display(df_team.loc[:, [col_type in colname for colname in dataframe_maker.df_sport_teams_.columns]].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97927320",
   "metadata": {},
   "source": [
    "#### 1.2.3 Проверка типа RGB_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "facf6c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Базовые статистики типа RGB_hist для команды 0\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RGB_hist_r_bin_0</th>\n",
       "      <th>RGB_hist_r_bin_1</th>\n",
       "      <th>RGB_hist_r_bin_2</th>\n",
       "      <th>RGB_hist_r_bin_3</th>\n",
       "      <th>RGB_hist_r_bin_4</th>\n",
       "      <th>RGB_hist_r_bin_5</th>\n",
       "      <th>RGB_hist_r_bin_6</th>\n",
       "      <th>RGB_hist_r_bin_7</th>\n",
       "      <th>RGB_hist_r_bin_8</th>\n",
       "      <th>RGB_hist_r_bin_9</th>\n",
       "      <th>...</th>\n",
       "      <th>RGB_hist_b_bin_6</th>\n",
       "      <th>RGB_hist_b_bin_7</th>\n",
       "      <th>RGB_hist_b_bin_8</th>\n",
       "      <th>RGB_hist_b_bin_9</th>\n",
       "      <th>RGB_hist_b_bin_10</th>\n",
       "      <th>RGB_hist_b_bin_11</th>\n",
       "      <th>RGB_hist_b_bin_12</th>\n",
       "      <th>RGB_hist_b_bin_13</th>\n",
       "      <th>RGB_hist_b_bin_14</th>\n",
       "      <th>RGB_hist_b_bin_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>187.808765</td>\n",
       "      <td>657.364542</td>\n",
       "      <td>1042.595618</td>\n",
       "      <td>976.950199</td>\n",
       "      <td>1229.231076</td>\n",
       "      <td>874.187251</td>\n",
       "      <td>687.665339</td>\n",
       "      <td>641.798805</td>\n",
       "      <td>649.386454</td>\n",
       "      <td>733.067729</td>\n",
       "      <td>...</td>\n",
       "      <td>1911.404382</td>\n",
       "      <td>1937.227092</td>\n",
       "      <td>1936.103586</td>\n",
       "      <td>1370.593625</td>\n",
       "      <td>830.677291</td>\n",
       "      <td>582.041833</td>\n",
       "      <td>445.541833</td>\n",
       "      <td>362.675299</td>\n",
       "      <td>348.844622</td>\n",
       "      <td>830.697211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>253.055356</td>\n",
       "      <td>590.031296</td>\n",
       "      <td>735.259204</td>\n",
       "      <td>579.209437</td>\n",
       "      <td>922.295469</td>\n",
       "      <td>641.562430</td>\n",
       "      <td>420.002333</td>\n",
       "      <td>362.623201</td>\n",
       "      <td>336.337426</td>\n",
       "      <td>407.702960</td>\n",
       "      <td>...</td>\n",
       "      <td>1114.553689</td>\n",
       "      <td>1258.145807</td>\n",
       "      <td>1362.448775</td>\n",
       "      <td>1183.369818</td>\n",
       "      <td>557.800410</td>\n",
       "      <td>340.475352</td>\n",
       "      <td>263.101677</td>\n",
       "      <td>233.845899</td>\n",
       "      <td>247.950988</td>\n",
       "      <td>816.606800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>255.500000</td>\n",
       "      <td>525.250000</td>\n",
       "      <td>592.250000</td>\n",
       "      <td>578.750000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>392.250000</td>\n",
       "      <td>366.250000</td>\n",
       "      <td>384.250000</td>\n",
       "      <td>420.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>1099.750000</td>\n",
       "      <td>1073.000000</td>\n",
       "      <td>952.500000</td>\n",
       "      <td>691.250000</td>\n",
       "      <td>481.250000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>187.250000</td>\n",
       "      <td>166.250000</td>\n",
       "      <td>254.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>144.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>864.500000</td>\n",
       "      <td>867.000000</td>\n",
       "      <td>934.500000</td>\n",
       "      <td>727.000000</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>585.500000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1648.000000</td>\n",
       "      <td>1707.500000</td>\n",
       "      <td>1570.000000</td>\n",
       "      <td>1019.500000</td>\n",
       "      <td>676.000000</td>\n",
       "      <td>496.500000</td>\n",
       "      <td>384.500000</td>\n",
       "      <td>310.500000</td>\n",
       "      <td>281.500000</td>\n",
       "      <td>580.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>227.750000</td>\n",
       "      <td>819.750000</td>\n",
       "      <td>1377.750000</td>\n",
       "      <td>1210.750000</td>\n",
       "      <td>1623.000000</td>\n",
       "      <td>1111.500000</td>\n",
       "      <td>904.250000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>868.000000</td>\n",
       "      <td>944.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>2435.500000</td>\n",
       "      <td>2382.750000</td>\n",
       "      <td>2341.750000</td>\n",
       "      <td>1558.000000</td>\n",
       "      <td>992.500000</td>\n",
       "      <td>742.750000</td>\n",
       "      <td>598.000000</td>\n",
       "      <td>489.250000</td>\n",
       "      <td>493.250000</td>\n",
       "      <td>1131.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2871.000000</td>\n",
       "      <td>4133.000000</td>\n",
       "      <td>4890.000000</td>\n",
       "      <td>4340.000000</td>\n",
       "      <td>5047.000000</td>\n",
       "      <td>3619.000000</td>\n",
       "      <td>2632.000000</td>\n",
       "      <td>2190.000000</td>\n",
       "      <td>2083.000000</td>\n",
       "      <td>2740.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7450.000000</td>\n",
       "      <td>13219.000000</td>\n",
       "      <td>8079.000000</td>\n",
       "      <td>9974.000000</td>\n",
       "      <td>4280.000000</td>\n",
       "      <td>2392.000000</td>\n",
       "      <td>1604.000000</td>\n",
       "      <td>1535.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>5955.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RGB_hist_r_bin_0  RGB_hist_r_bin_1  RGB_hist_r_bin_2  RGB_hist_r_bin_3  \\\n",
       "count        502.000000        502.000000        502.000000        502.000000   \n",
       "mean         187.808765        657.364542       1042.595618        976.950199   \n",
       "std          253.055356        590.031296        735.259204        579.209437   \n",
       "min            0.000000          8.000000         36.000000         70.000000   \n",
       "25%           62.000000        255.500000        525.250000        592.250000   \n",
       "50%          144.000000        501.000000        864.500000        867.000000   \n",
       "75%          227.750000        819.750000       1377.750000       1210.750000   \n",
       "max         2871.000000       4133.000000       4890.000000       4340.000000   \n",
       "\n",
       "       RGB_hist_r_bin_4  RGB_hist_r_bin_5  RGB_hist_r_bin_6  RGB_hist_r_bin_7  \\\n",
       "count        502.000000        502.000000        502.000000        502.000000   \n",
       "mean        1229.231076        874.187251        687.665339        641.798805   \n",
       "std          922.295469        641.562430        420.002333        362.623201   \n",
       "min           75.000000         79.000000         88.000000        123.000000   \n",
       "25%          578.750000        414.000000        392.250000        366.250000   \n",
       "50%          934.500000        727.000000        584.000000        579.000000   \n",
       "75%         1623.000000       1111.500000        904.250000        840.000000   \n",
       "max         5047.000000       3619.000000       2632.000000       2190.000000   \n",
       "\n",
       "       RGB_hist_r_bin_8  RGB_hist_r_bin_9  ...  RGB_hist_b_bin_6  \\\n",
       "count        502.000000        502.000000  ...        502.000000   \n",
       "mean         649.386454        733.067729  ...       1911.404382   \n",
       "std          336.337426        407.702960  ...       1114.553689   \n",
       "min          143.000000        143.000000  ...        351.000000   \n",
       "25%          384.250000        420.750000  ...       1099.750000   \n",
       "50%          585.500000        644.000000  ...       1648.000000   \n",
       "75%          868.000000        944.500000  ...       2435.500000   \n",
       "max         2083.000000       2740.000000  ...       7450.000000   \n",
       "\n",
       "       RGB_hist_b_bin_7  RGB_hist_b_bin_8  RGB_hist_b_bin_9  \\\n",
       "count        502.000000        502.000000        502.000000   \n",
       "mean        1937.227092       1936.103586       1370.593625   \n",
       "std         1258.145807       1362.448775       1183.369818   \n",
       "min          433.000000        261.000000        187.000000   \n",
       "25%         1073.000000        952.500000        691.250000   \n",
       "50%         1707.500000       1570.000000       1019.500000   \n",
       "75%         2382.750000       2341.750000       1558.000000   \n",
       "max        13219.000000       8079.000000       9974.000000   \n",
       "\n",
       "       RGB_hist_b_bin_10  RGB_hist_b_bin_11  RGB_hist_b_bin_12  \\\n",
       "count         502.000000         502.000000         502.000000   \n",
       "mean          830.677291         582.041833         445.541833   \n",
       "std           557.800410         340.475352         263.101677   \n",
       "min           106.000000          75.000000          51.000000   \n",
       "25%           481.250000         337.000000         255.000000   \n",
       "50%           676.000000         496.500000         384.500000   \n",
       "75%           992.500000         742.750000         598.000000   \n",
       "max          4280.000000        2392.000000        1604.000000   \n",
       "\n",
       "       RGB_hist_b_bin_13  RGB_hist_b_bin_14  RGB_hist_b_bin_15  \n",
       "count         502.000000         502.000000         502.000000  \n",
       "mean          362.675299         348.844622         830.697211  \n",
       "std           233.845899         247.950988         816.606800  \n",
       "min            21.000000          10.000000           0.000000  \n",
       "25%           187.250000         166.250000         254.250000  \n",
       "50%           310.500000         281.500000         580.000000  \n",
       "75%           489.250000         493.250000        1131.000000  \n",
       "max          1535.000000        1568.000000        5955.000000  \n",
       "\n",
       "[8 rows x 48 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Базовые статистики типа RGB_hist для команды 1\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RGB_hist_r_bin_0</th>\n",
       "      <th>RGB_hist_r_bin_1</th>\n",
       "      <th>RGB_hist_r_bin_2</th>\n",
       "      <th>RGB_hist_r_bin_3</th>\n",
       "      <th>RGB_hist_r_bin_4</th>\n",
       "      <th>RGB_hist_r_bin_5</th>\n",
       "      <th>RGB_hist_r_bin_6</th>\n",
       "      <th>RGB_hist_r_bin_7</th>\n",
       "      <th>RGB_hist_r_bin_8</th>\n",
       "      <th>RGB_hist_r_bin_9</th>\n",
       "      <th>...</th>\n",
       "      <th>RGB_hist_b_bin_6</th>\n",
       "      <th>RGB_hist_b_bin_7</th>\n",
       "      <th>RGB_hist_b_bin_8</th>\n",
       "      <th>RGB_hist_b_bin_9</th>\n",
       "      <th>RGB_hist_b_bin_10</th>\n",
       "      <th>RGB_hist_b_bin_11</th>\n",
       "      <th>RGB_hist_b_bin_12</th>\n",
       "      <th>RGB_hist_b_bin_13</th>\n",
       "      <th>RGB_hist_b_bin_14</th>\n",
       "      <th>RGB_hist_b_bin_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>342.759036</td>\n",
       "      <td>1456.291165</td>\n",
       "      <td>2172.339357</td>\n",
       "      <td>1363.885542</td>\n",
       "      <td>1332.662651</td>\n",
       "      <td>956.827309</td>\n",
       "      <td>748.056225</td>\n",
       "      <td>623.544177</td>\n",
       "      <td>555.321285</td>\n",
       "      <td>560.399598</td>\n",
       "      <td>...</td>\n",
       "      <td>1707.287149</td>\n",
       "      <td>1803.383534</td>\n",
       "      <td>1640.546185</td>\n",
       "      <td>1032.700803</td>\n",
       "      <td>607.098394</td>\n",
       "      <td>427.447791</td>\n",
       "      <td>338.265060</td>\n",
       "      <td>275.345382</td>\n",
       "      <td>257.152610</td>\n",
       "      <td>644.708835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>294.252527</td>\n",
       "      <td>1123.280766</td>\n",
       "      <td>1066.469785</td>\n",
       "      <td>717.422012</td>\n",
       "      <td>1035.042457</td>\n",
       "      <td>648.123049</td>\n",
       "      <td>389.118708</td>\n",
       "      <td>324.256095</td>\n",
       "      <td>303.701374</td>\n",
       "      <td>347.728731</td>\n",
       "      <td>...</td>\n",
       "      <td>1039.027381</td>\n",
       "      <td>1437.922442</td>\n",
       "      <td>1513.707430</td>\n",
       "      <td>995.536783</td>\n",
       "      <td>470.466843</td>\n",
       "      <td>292.915701</td>\n",
       "      <td>236.283783</td>\n",
       "      <td>211.027431</td>\n",
       "      <td>231.461312</td>\n",
       "      <td>795.347731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>730.750000</td>\n",
       "      <td>1454.500000</td>\n",
       "      <td>889.250000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>537.250000</td>\n",
       "      <td>489.250000</td>\n",
       "      <td>402.250000</td>\n",
       "      <td>333.250000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>921.000000</td>\n",
       "      <td>887.250000</td>\n",
       "      <td>641.750000</td>\n",
       "      <td>422.000000</td>\n",
       "      <td>295.250000</td>\n",
       "      <td>208.250000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>76.750000</td>\n",
       "      <td>109.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>291.500000</td>\n",
       "      <td>1147.500000</td>\n",
       "      <td>1980.500000</td>\n",
       "      <td>1162.000000</td>\n",
       "      <td>983.000000</td>\n",
       "      <td>777.500000</td>\n",
       "      <td>662.500000</td>\n",
       "      <td>529.500000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1459.500000</td>\n",
       "      <td>1373.000000</td>\n",
       "      <td>1084.500000</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>353.500000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>227.500000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>347.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>424.750000</td>\n",
       "      <td>1813.750000</td>\n",
       "      <td>2672.000000</td>\n",
       "      <td>1603.500000</td>\n",
       "      <td>1637.500000</td>\n",
       "      <td>1140.500000</td>\n",
       "      <td>878.750000</td>\n",
       "      <td>772.750000</td>\n",
       "      <td>715.000000</td>\n",
       "      <td>733.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>2193.750000</td>\n",
       "      <td>2198.500000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1198.750000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>573.500000</td>\n",
       "      <td>474.250000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>377.750000</td>\n",
       "      <td>907.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3193.000000</td>\n",
       "      <td>9827.000000</td>\n",
       "      <td>8431.000000</td>\n",
       "      <td>5250.000000</td>\n",
       "      <td>7351.000000</td>\n",
       "      <td>4160.000000</td>\n",
       "      <td>2457.000000</td>\n",
       "      <td>2722.000000</td>\n",
       "      <td>2380.000000</td>\n",
       "      <td>2206.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5982.000000</td>\n",
       "      <td>11535.000000</td>\n",
       "      <td>10523.000000</td>\n",
       "      <td>6272.000000</td>\n",
       "      <td>3918.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>1432.000000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>6768.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RGB_hist_r_bin_0  RGB_hist_r_bin_1  RGB_hist_r_bin_2  RGB_hist_r_bin_3  \\\n",
       "count        498.000000        498.000000        498.000000        498.000000   \n",
       "mean         342.759036       1456.291165       2172.339357       1363.885542   \n",
       "std          294.252527       1123.280766       1066.469785        717.422012   \n",
       "min            0.000000         26.000000        345.000000        342.000000   \n",
       "25%          183.500000        730.750000       1454.500000        889.250000   \n",
       "50%          291.500000       1147.500000       1980.500000       1162.000000   \n",
       "75%          424.750000       1813.750000       2672.000000       1603.500000   \n",
       "max         3193.000000       9827.000000       8431.000000       5250.000000   \n",
       "\n",
       "       RGB_hist_r_bin_4  RGB_hist_r_bin_5  RGB_hist_r_bin_6  RGB_hist_r_bin_7  \\\n",
       "count        498.000000        498.000000        498.000000        498.000000   \n",
       "mean        1332.662651        956.827309        748.056225        623.544177   \n",
       "std         1035.042457        648.123049        389.118708        324.256095   \n",
       "min          218.000000        138.000000        161.000000        165.000000   \n",
       "25%          666.000000        537.250000        489.250000        402.250000   \n",
       "50%          983.000000        777.500000        662.500000        529.500000   \n",
       "75%         1637.500000       1140.500000        878.750000        772.750000   \n",
       "max         7351.000000       4160.000000       2457.000000       2722.000000   \n",
       "\n",
       "       RGB_hist_r_bin_8  RGB_hist_r_bin_9  ...  RGB_hist_b_bin_6  \\\n",
       "count        498.000000        498.000000  ...        498.000000   \n",
       "mean         555.321285        560.399598  ...       1707.287149   \n",
       "std          303.701374        347.728731  ...       1039.027381   \n",
       "min          125.000000         89.000000  ...        238.000000   \n",
       "25%          333.250000        294.000000  ...        921.000000   \n",
       "50%          498.000000        493.000000  ...       1459.500000   \n",
       "75%          715.000000        733.750000  ...       2193.750000   \n",
       "max         2380.000000       2206.000000  ...       5982.000000   \n",
       "\n",
       "       RGB_hist_b_bin_7  RGB_hist_b_bin_8  RGB_hist_b_bin_9  \\\n",
       "count        498.000000        498.000000        498.000000   \n",
       "mean        1803.383534       1640.546185       1032.700803   \n",
       "std         1437.922442       1513.707430        995.536783   \n",
       "min          201.000000         63.000000         21.000000   \n",
       "25%          887.250000        641.750000        422.000000   \n",
       "50%         1373.000000       1084.500000        716.000000   \n",
       "75%         2198.500000       2015.000000       1198.750000   \n",
       "max        11535.000000      10523.000000       6272.000000   \n",
       "\n",
       "       RGB_hist_b_bin_10  RGB_hist_b_bin_11  RGB_hist_b_bin_12  \\\n",
       "count         498.000000         498.000000         498.000000   \n",
       "mean          607.098394         427.447791         338.265060   \n",
       "std           470.466843         292.915701         236.283783   \n",
       "min            16.000000           1.000000           1.000000   \n",
       "25%           295.250000         208.250000         154.000000   \n",
       "50%           466.000000         353.500000         294.000000   \n",
       "75%           776.000000         573.500000         474.250000   \n",
       "max          3918.000000        1997.000000        1708.000000   \n",
       "\n",
       "       RGB_hist_b_bin_13  RGB_hist_b_bin_14  RGB_hist_b_bin_15  \n",
       "count         498.000000         498.000000         498.000000  \n",
       "mean          275.345382         257.152610         644.708835  \n",
       "std           211.027431         231.461312         795.347731  \n",
       "min             0.000000           0.000000           0.000000  \n",
       "25%           109.000000          76.750000         109.250000  \n",
       "50%           227.500000         187.000000         347.500000  \n",
       "75%           383.000000         377.750000         907.750000  \n",
       "max          1432.000000        1490.000000        6768.000000  \n",
       "\n",
       "[8 rows x 48 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_type = 'RGB_hist'\n",
    "for idx, df_team in enumerate(df_teams_list):\n",
    "    print(f\"Базовые статистики типа {col_type} для команды {idx}\")\n",
    "    display(df_team.loc[:, [col_type in colname for colname in dataframe_maker.df_sport_teams_.columns]].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08fe03e8",
   "metadata": {},
   "source": [
    "#### 1.2.4 Проверка типа HSV_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7b07a9fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Базовые статистики типа HSV_hist для команды 0\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HSV_hist_h_bin_0</th>\n",
       "      <th>HSV_hist_h_bin_1</th>\n",
       "      <th>HSV_hist_h_bin_2</th>\n",
       "      <th>HSV_hist_h_bin_3</th>\n",
       "      <th>HSV_hist_h_bin_4</th>\n",
       "      <th>HSV_hist_h_bin_5</th>\n",
       "      <th>HSV_hist_h_bin_6</th>\n",
       "      <th>HSV_hist_h_bin_7</th>\n",
       "      <th>HSV_hist_h_bin_8</th>\n",
       "      <th>HSV_hist_h_bin_9</th>\n",
       "      <th>...</th>\n",
       "      <th>HSV_hist_v_tbin_6</th>\n",
       "      <th>HSV_hist_v_tbin_7</th>\n",
       "      <th>HSV_hist_v_tbin_8</th>\n",
       "      <th>HSV_hist_v_tbin_9</th>\n",
       "      <th>HSV_hist_v_tbin_10</th>\n",
       "      <th>HSV_hist_v_tbin_11</th>\n",
       "      <th>HSV_hist_v_tbin_12</th>\n",
       "      <th>HSV_hist_v_tbin_13</th>\n",
       "      <th>HSV_hist_v_tbin_14</th>\n",
       "      <th>HSV_hist_v_tbin_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3759.063745</td>\n",
       "      <td>8387.569721</td>\n",
       "      <td>32.868526</td>\n",
       "      <td>9.276892</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>54.043825</td>\n",
       "      <td>159.868526</td>\n",
       "      <td>1293.938247</td>\n",
       "      <td>2228.671315</td>\n",
       "      <td>985.625498</td>\n",
       "      <td>...</td>\n",
       "      <td>969.346614</td>\n",
       "      <td>1064.099602</td>\n",
       "      <td>892.338645</td>\n",
       "      <td>808.041833</td>\n",
       "      <td>1007.515936</td>\n",
       "      <td>1414.647410</td>\n",
       "      <td>1700.418327</td>\n",
       "      <td>1640.733068</td>\n",
       "      <td>1394.519920</td>\n",
       "      <td>3420.862550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1805.260283</td>\n",
       "      <td>5509.610762</td>\n",
       "      <td>101.306567</td>\n",
       "      <td>35.577766</td>\n",
       "      <td>56.755059</td>\n",
       "      <td>164.522141</td>\n",
       "      <td>319.997016</td>\n",
       "      <td>1252.827941</td>\n",
       "      <td>1753.178394</td>\n",
       "      <td>670.110575</td>\n",
       "      <td>...</td>\n",
       "      <td>646.180706</td>\n",
       "      <td>770.322380</td>\n",
       "      <td>626.939438</td>\n",
       "      <td>459.045804</td>\n",
       "      <td>637.012992</td>\n",
       "      <td>982.044963</td>\n",
       "      <td>1215.184658</td>\n",
       "      <td>1230.875386</td>\n",
       "      <td>1235.205275</td>\n",
       "      <td>3388.552992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>679.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2417.500000</td>\n",
       "      <td>4296.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>351.500000</td>\n",
       "      <td>944.000000</td>\n",
       "      <td>544.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>501.750000</td>\n",
       "      <td>489.500000</td>\n",
       "      <td>449.250000</td>\n",
       "      <td>435.250000</td>\n",
       "      <td>491.750000</td>\n",
       "      <td>681.500000</td>\n",
       "      <td>744.500000</td>\n",
       "      <td>698.500000</td>\n",
       "      <td>613.500000</td>\n",
       "      <td>1101.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3383.000000</td>\n",
       "      <td>6954.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>956.500000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>851.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>791.000000</td>\n",
       "      <td>823.500000</td>\n",
       "      <td>722.000000</td>\n",
       "      <td>715.500000</td>\n",
       "      <td>871.500000</td>\n",
       "      <td>1194.500000</td>\n",
       "      <td>1434.000000</td>\n",
       "      <td>1271.500000</td>\n",
       "      <td>1006.500000</td>\n",
       "      <td>2151.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4613.750000</td>\n",
       "      <td>11291.750000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>133.750000</td>\n",
       "      <td>1791.750000</td>\n",
       "      <td>3159.250000</td>\n",
       "      <td>1278.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>1266.750000</td>\n",
       "      <td>1438.250000</td>\n",
       "      <td>1150.250000</td>\n",
       "      <td>1081.000000</td>\n",
       "      <td>1343.000000</td>\n",
       "      <td>1873.500000</td>\n",
       "      <td>2302.250000</td>\n",
       "      <td>2203.000000</td>\n",
       "      <td>1777.000000</td>\n",
       "      <td>4474.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11415.000000</td>\n",
       "      <td>29651.000000</td>\n",
       "      <td>1616.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>634.000000</td>\n",
       "      <td>2091.000000</td>\n",
       "      <td>1945.000000</td>\n",
       "      <td>6868.000000</td>\n",
       "      <td>8364.000000</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3909.000000</td>\n",
       "      <td>4833.000000</td>\n",
       "      <td>4037.000000</td>\n",
       "      <td>2857.000000</td>\n",
       "      <td>4555.000000</td>\n",
       "      <td>6976.000000</td>\n",
       "      <td>7601.000000</td>\n",
       "      <td>6313.000000</td>\n",
       "      <td>10676.000000</td>\n",
       "      <td>18798.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HSV_hist_h_bin_0  HSV_hist_h_bin_1  HSV_hist_h_bin_2  HSV_hist_h_bin_3  \\\n",
       "count        502.000000        502.000000        502.000000        502.000000   \n",
       "mean        3759.063745       8387.569721         32.868526          9.276892   \n",
       "std         1805.260283       5509.610762        101.306567         35.577766   \n",
       "min          679.000000        399.000000          0.000000          0.000000   \n",
       "25%         2417.500000       4296.000000          0.000000          0.000000   \n",
       "50%         3383.000000       6954.500000          8.000000          0.000000   \n",
       "75%         4613.750000      11291.750000         30.000000          5.000000   \n",
       "max        11415.000000      29651.000000       1616.000000        550.000000   \n",
       "\n",
       "       HSV_hist_h_bin_4  HSV_hist_h_bin_5  HSV_hist_h_bin_6  HSV_hist_h_bin_7  \\\n",
       "count        502.000000        502.000000        502.000000        502.000000   \n",
       "mean          15.500000         54.043825        159.868526       1293.938247   \n",
       "std           56.755059        164.522141        319.997016       1252.827941   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000        351.500000   \n",
       "50%            1.000000          3.000000         24.500000        956.500000   \n",
       "75%            8.000000         21.000000        133.750000       1791.750000   \n",
       "max          634.000000       2091.000000       1945.000000       6868.000000   \n",
       "\n",
       "       HSV_hist_h_bin_8  HSV_hist_h_bin_9  ...  HSV_hist_v_tbin_6  \\\n",
       "count        502.000000        502.000000  ...         502.000000   \n",
       "mean        2228.671315        985.625498  ...         969.346614   \n",
       "std         1753.178394        670.110575  ...         646.180706   \n",
       "min            0.000000         17.000000  ...          86.000000   \n",
       "25%          944.000000        544.000000  ...         501.750000   \n",
       "50%         1760.000000        851.500000  ...         791.000000   \n",
       "75%         3159.250000       1278.750000  ...        1266.750000   \n",
       "max         8364.000000       6500.000000  ...        3909.000000   \n",
       "\n",
       "       HSV_hist_v_tbin_7  HSV_hist_v_tbin_8  HSV_hist_v_tbin_9  \\\n",
       "count         502.000000         502.000000         502.000000   \n",
       "mean         1064.099602         892.338645         808.041833   \n",
       "std           770.322380         626.939438         459.045804   \n",
       "min           127.000000         145.000000         162.000000   \n",
       "25%           489.500000         449.250000         435.250000   \n",
       "50%           823.500000         722.000000         715.500000   \n",
       "75%          1438.250000        1150.250000        1081.000000   \n",
       "max          4833.000000        4037.000000        2857.000000   \n",
       "\n",
       "       HSV_hist_v_tbin_10  HSV_hist_v_tbin_11  HSV_hist_v_tbin_12  \\\n",
       "count          502.000000          502.000000          502.000000   \n",
       "mean          1007.515936         1414.647410         1700.418327   \n",
       "std            637.012992          982.044963         1215.184658   \n",
       "min            134.000000          124.000000          134.000000   \n",
       "25%            491.750000          681.500000          744.500000   \n",
       "50%            871.500000         1194.500000         1434.000000   \n",
       "75%           1343.000000         1873.500000         2302.250000   \n",
       "max           4555.000000         6976.000000         7601.000000   \n",
       "\n",
       "       HSV_hist_v_tbin_13  HSV_hist_v_tbin_14  HSV_hist_v_tbin_15  \n",
       "count          502.000000          502.000000          502.000000  \n",
       "mean          1640.733068         1394.519920         3420.862550  \n",
       "std           1230.875386         1235.205275         3388.552992  \n",
       "min            165.000000           49.000000           13.000000  \n",
       "25%            698.500000          613.500000         1101.750000  \n",
       "50%           1271.500000         1006.500000         2151.500000  \n",
       "75%           2203.000000         1777.000000         4474.500000  \n",
       "max           6313.000000        10676.000000        18798.000000  \n",
       "\n",
       "[8 rows x 48 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Базовые статистики типа HSV_hist для команды 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HSV_hist_h_bin_0</th>\n",
       "      <th>HSV_hist_h_bin_1</th>\n",
       "      <th>HSV_hist_h_bin_2</th>\n",
       "      <th>HSV_hist_h_bin_3</th>\n",
       "      <th>HSV_hist_h_bin_4</th>\n",
       "      <th>HSV_hist_h_bin_5</th>\n",
       "      <th>HSV_hist_h_bin_6</th>\n",
       "      <th>HSV_hist_h_bin_7</th>\n",
       "      <th>HSV_hist_h_bin_8</th>\n",
       "      <th>HSV_hist_h_bin_9</th>\n",
       "      <th>...</th>\n",
       "      <th>HSV_hist_v_tbin_6</th>\n",
       "      <th>HSV_hist_v_tbin_7</th>\n",
       "      <th>HSV_hist_v_tbin_8</th>\n",
       "      <th>HSV_hist_v_tbin_9</th>\n",
       "      <th>HSV_hist_v_tbin_10</th>\n",
       "      <th>HSV_hist_v_tbin_11</th>\n",
       "      <th>HSV_hist_v_tbin_12</th>\n",
       "      <th>HSV_hist_v_tbin_13</th>\n",
       "      <th>HSV_hist_v_tbin_14</th>\n",
       "      <th>HSV_hist_v_tbin_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4016.158635</td>\n",
       "      <td>6700.291165</td>\n",
       "      <td>54.799197</td>\n",
       "      <td>18.678715</td>\n",
       "      <td>30.863454</td>\n",
       "      <td>60.138554</td>\n",
       "      <td>208.018072</td>\n",
       "      <td>1870.397590</td>\n",
       "      <td>2648.819277</td>\n",
       "      <td>1093.867470</td>\n",
       "      <td>...</td>\n",
       "      <td>1082.355422</td>\n",
       "      <td>1046.787149</td>\n",
       "      <td>798.313253</td>\n",
       "      <td>647.285141</td>\n",
       "      <td>742.405622</td>\n",
       "      <td>1010.172691</td>\n",
       "      <td>1200.819277</td>\n",
       "      <td>1287.769076</td>\n",
       "      <td>1220.433735</td>\n",
       "      <td>2684.240964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1774.021238</td>\n",
       "      <td>5193.553545</td>\n",
       "      <td>112.851837</td>\n",
       "      <td>58.785984</td>\n",
       "      <td>101.525752</td>\n",
       "      <td>183.775437</td>\n",
       "      <td>343.574033</td>\n",
       "      <td>1668.743155</td>\n",
       "      <td>2085.083686</td>\n",
       "      <td>742.940998</td>\n",
       "      <td>...</td>\n",
       "      <td>739.490460</td>\n",
       "      <td>798.011905</td>\n",
       "      <td>598.245122</td>\n",
       "      <td>415.986519</td>\n",
       "      <td>555.997955</td>\n",
       "      <td>864.062057</td>\n",
       "      <td>960.831726</td>\n",
       "      <td>1161.377858</td>\n",
       "      <td>1289.033205</td>\n",
       "      <td>3104.725828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>718.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2788.500000</td>\n",
       "      <td>2842.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>657.000000</td>\n",
       "      <td>1218.000000</td>\n",
       "      <td>623.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>615.750000</td>\n",
       "      <td>524.750000</td>\n",
       "      <td>393.250000</td>\n",
       "      <td>318.250000</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>362.250000</td>\n",
       "      <td>474.000000</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>367.500000</td>\n",
       "      <td>641.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3652.000000</td>\n",
       "      <td>5480.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>1389.000000</td>\n",
       "      <td>2027.000000</td>\n",
       "      <td>934.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>844.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>623.500000</td>\n",
       "      <td>554.000000</td>\n",
       "      <td>561.000000</td>\n",
       "      <td>753.500000</td>\n",
       "      <td>909.500000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>725.500000</td>\n",
       "      <td>1581.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5066.250000</td>\n",
       "      <td>8940.750000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>34.750000</td>\n",
       "      <td>216.250000</td>\n",
       "      <td>2449.250000</td>\n",
       "      <td>3576.500000</td>\n",
       "      <td>1326.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>1276.250000</td>\n",
       "      <td>1292.250000</td>\n",
       "      <td>1010.250000</td>\n",
       "      <td>888.250000</td>\n",
       "      <td>997.750000</td>\n",
       "      <td>1337.750000</td>\n",
       "      <td>1756.000000</td>\n",
       "      <td>1717.250000</td>\n",
       "      <td>1654.250000</td>\n",
       "      <td>3568.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12909.000000</td>\n",
       "      <td>25173.000000</td>\n",
       "      <td>1255.000000</td>\n",
       "      <td>654.000000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>2856.000000</td>\n",
       "      <td>2529.000000</td>\n",
       "      <td>8782.000000</td>\n",
       "      <td>13640.000000</td>\n",
       "      <td>6756.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5447.000000</td>\n",
       "      <td>6771.000000</td>\n",
       "      <td>4275.000000</td>\n",
       "      <td>2724.000000</td>\n",
       "      <td>3881.000000</td>\n",
       "      <td>5037.000000</td>\n",
       "      <td>4870.000000</td>\n",
       "      <td>8174.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>19947.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HSV_hist_h_bin_0  HSV_hist_h_bin_1  HSV_hist_h_bin_2  HSV_hist_h_bin_3  \\\n",
       "count        498.000000        498.000000        498.000000        498.000000   \n",
       "mean        4016.158635       6700.291165         54.799197         18.678715   \n",
       "std         1774.021238       5193.553545        112.851837         58.785984   \n",
       "min          718.000000         62.000000          0.000000          0.000000   \n",
       "25%         2788.500000       2842.500000          3.000000          0.000000   \n",
       "50%         3652.000000       5480.000000         19.000000          2.000000   \n",
       "75%         5066.250000       8940.750000         52.000000         11.000000   \n",
       "max        12909.000000      25173.000000       1255.000000        654.000000   \n",
       "\n",
       "       HSV_hist_h_bin_4  HSV_hist_h_bin_5  HSV_hist_h_bin_6  HSV_hist_h_bin_7  \\\n",
       "count        498.000000        498.000000        498.000000        498.000000   \n",
       "mean          30.863454         60.138554        208.018072       1870.397590   \n",
       "std          101.525752        183.775437        343.574033       1668.743155   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000         10.000000        657.000000   \n",
       "50%            5.000000          3.000000         64.500000       1389.000000   \n",
       "75%           20.000000         34.750000        216.250000       2449.250000   \n",
       "max         1168.000000       2856.000000       2529.000000       8782.000000   \n",
       "\n",
       "       HSV_hist_h_bin_8  HSV_hist_h_bin_9  ...  HSV_hist_v_tbin_6  \\\n",
       "count        498.000000        498.000000  ...         498.000000   \n",
       "mean        2648.819277       1093.867470  ...        1082.355422   \n",
       "std         2085.083686        742.940998  ...         739.490460   \n",
       "min            0.000000          6.000000  ...         161.000000   \n",
       "25%         1218.000000        623.750000  ...         615.750000   \n",
       "50%         2027.000000        934.500000  ...         844.000000   \n",
       "75%         3576.500000       1326.500000  ...        1276.250000   \n",
       "max        13640.000000       6756.000000  ...        5447.000000   \n",
       "\n",
       "       HSV_hist_v_tbin_7  HSV_hist_v_tbin_8  HSV_hist_v_tbin_9  \\\n",
       "count         498.000000         498.000000         498.000000   \n",
       "mean         1046.787149         798.313253         647.285141   \n",
       "std           798.011905         598.245122         415.986519   \n",
       "min           165.000000         146.000000         100.000000   \n",
       "25%           524.750000         393.250000         318.250000   \n",
       "50%           795.000000         623.500000         554.000000   \n",
       "75%          1292.250000        1010.250000         888.250000   \n",
       "max          6771.000000        4275.000000        2724.000000   \n",
       "\n",
       "       HSV_hist_v_tbin_10  HSV_hist_v_tbin_11  HSV_hist_v_tbin_12  \\\n",
       "count          498.000000          498.000000          498.000000   \n",
       "mean           742.405622         1010.172691         1200.819277   \n",
       "std            555.997955          864.062057          960.831726   \n",
       "min             50.000000           13.000000            7.000000   \n",
       "25%            315.500000          362.250000          474.000000   \n",
       "50%            561.000000          753.500000          909.500000   \n",
       "75%            997.750000         1337.750000         1756.000000   \n",
       "max           3881.000000         5037.000000         4870.000000   \n",
       "\n",
       "       HSV_hist_v_tbin_13  HSV_hist_v_tbin_14  HSV_hist_v_tbin_15  \n",
       "count          498.000000          498.000000          498.000000  \n",
       "mean          1287.769076         1220.433735         2684.240964  \n",
       "std           1161.377858         1289.033205         3104.725828  \n",
       "min              0.000000            0.000000            0.000000  \n",
       "25%            460.000000          367.500000          641.500000  \n",
       "50%            886.000000          725.500000         1581.000000  \n",
       "75%           1717.250000         1654.250000         3568.500000  \n",
       "max           8174.000000         8349.000000        19947.000000  \n",
       "\n",
       "[8 rows x 48 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_type = 'HSV_hist'\n",
    "for idx, df_team in enumerate(df_teams_list):\n",
    "    print(f\"Базовые статистики типа {col_type} для команды {idx}\")\n",
    "    display(df_team.loc[:, [col_type in colname for colname in dataframe_maker.df_sport_teams_.columns]].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c42b05",
   "metadata": {},
   "source": [
    "Из полученных результатов видно, что базовые статистики различаются по командам и все они могут помочь при обучении модели."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c0d5065",
   "metadata": {},
   "source": [
    "## 1.3 Проверка корреляции признаков\n",
    "\n",
    "В этом разделе посмотрим какие признаки хорошо коррелируют с целевой переменной target. Дальше при обучении можно попробовать оставить только те признаки, которые дают хорошую корреляцию.\n",
    "\n",
    "### 1.3.1 Матрица корреляции типа признаков RGB_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3307dcb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица корреляции для типа признаков RGB_mean\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_type = 'RGB_mean'\n",
    "stat_col_names = [col_type in colname or colname == 'target' for colname in dataframe_maker.df_sport_teams_.columns]\n",
    "print(f\"Матрица корреляции для типа признаков {col_type}\")\n",
    "sns.heatmap(dataframe_maker.df_sport_teams_.loc[:, stat_col_names].corr(method='kendall'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb3796b",
   "metadata": {},
   "source": [
    "Из результатов матрицы корреляции средних значений по каналам RGB видно, что у признаков вообще отсутствует какая-либо корреляция. Можно сделать вывод, что эти признаки будут бесполезны для обучения модели.\n",
    "\n",
    "### 1.3.2 Матрица корреляции типа признаков HSV_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "266889a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица корреляции для типа признаков HSV_mean\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_type = 'HSV_mean'\n",
    "stat_col_names = [col_type in colname or colname == 'target' for colname in dataframe_maker.df_sport_teams_.columns]\n",
    "print(f\"Матрица корреляции для типа признаков {col_type}\")\n",
    "sns.heatmap(dataframe_maker.df_sport_teams_.loc[:, stat_col_names].corr(method='kendall'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6223d905",
   "metadata": {},
   "source": [
    "Из результатов матрицы корреляции средних значений по каналам HSV видно, что есть небольшая зависимость целевой переменной от Hue и Saturation, но Value не влияет никак. Его тоже можно убрать.\n",
    "\n",
    "### 1.3.3 Матрица корреляции типа признаков RGB_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4cf479e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица корреляции для типа признаков RGB_hist\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_type = 'RGB_hist'\n",
    "stat_col_names = [col_type in colname or colname == 'target' for colname in dataframe_maker.df_sport_teams_.columns]\n",
    "print(f\"Матрица корреляции для типа признаков {col_type}\")\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(dataframe_maker.df_sport_teams_.loc[:, stat_col_names].corr(method='kendall'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c70e287",
   "metadata": {},
   "source": [
    "Из результатов матрицы корреляции значений бинов гистограммы по каналам RGB можно сделать вывод, что по каналам RGB есть зависимость от целевой переменной примерно до 8 бина. Бины больше 8 можно попробовать удалить.\n",
    "\n",
    "### 1.3.4 Матрица корреляции типа признаков HSV_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d515e974",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица корреляции для типа признаков HSV_hist\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_type = 'HSV_hist'\n",
    "stat_col_names = [col_type in colname or colname == 'target' for colname in dataframe_maker.df_sport_teams_.columns]\n",
    "print(f\"Матрица корреляции для типа признаков {col_type}\")\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(dataframe_maker.df_sport_teams_.loc[:, stat_col_names].corr(method='spearman'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2314ef3f",
   "metadata": {},
   "source": [
    "Из результатов матрицы корреляции значений бинов гистограммы по каналам HSV можно сделать следующие выводы:\n",
    "- бины из Value менее всего коррелируют в целевой переменной, но есть хорошая корреляция в начальных бинах;\n",
    "- бины saturation и hue в целом имеют небольшую корреляцию с целевой переменой\n",
    "\n",
    "Также видны белые полосы, по данным значениям коэффициент не расчитался и равен np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae66e1c",
   "metadata": {},
   "source": [
    "## 2 Обучение модели\n",
    "\n",
    "Для решения задачи обучения модели был создан класс TeamClassifier.\n",
    "\n",
    "С помощью параметра model_class можно указать какой-либо класс модели из библиотеки sklearn. Модель обучается с помощью стратифицированной кросс-валидации. Указываем необходимое количество фолдов, для каждого фолда будет создан экземпляр модели выбранного класса. И каждая модель обучится на своем фолде. Для предсказаний будет использована каждая модель и результат будет выбираться по наибольшему количеству голосов для класса.\n",
    "\n",
    "В класс можно передавать как модели с обучением на целевой метке, так и модели для кластеризации. Для корректной работы требуется указать гиперпараметр класса model_type, который принимает значение training для моделей с обучением на целевой метке или clustering для моделей кластеризации.\n",
    "\n",
    "Также для многих моделей кластеризации придетя использовать метод fit_predict экземпляра класса TeamClassifier, вместо fit и predict по отдельности.\n",
    "\n",
    "Документацию можно посмотреть в файле **sport_team_lib.py**.\n",
    "\n",
    "Так же для предсказания реализован метод **predict_from_json()**, в котором можно указать путь до файла с разметкой боксов и путь к папке с изображениями. Модель сама преобразует данные в требуемый формат и выдаст предсказания.\n",
    "\n",
    "### 2.1 Обучение модели с учителем\n",
    "\n",
    "В данном разделе в качестве модели будем использовать **RandomForestClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5522d16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "team_classifier = TeamClassifier.load_from_json(bboxes_json=bboxes_json, image_path=image_path, \n",
    "                                                model_class=RandomForestClassifier,\n",
    "                                                model_params={'n_estimators': 300, \n",
    "                                                              'max_depth': 20, \n",
    "                                                              'random_state': SEED,\n",
    "                                                              'n_jobs': -1},\n",
    "                                                folds=10,\n",
    "                                                random_state=SEED)\n",
    "\n",
    "all_fit_feature_names = team_classifier.fit_features_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5a7560",
   "metadata": {},
   "source": [
    "#### 2.1.1 Модель с учителем с признаками типа RGB_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f963f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Mean Accuracy: 0.704\n"
     ]
    }
   ],
   "source": [
    "col_type = 'RGB_mean'\n",
    "team_classifier.fit_features_names = [colname for colname in all_fit_feature_names if col_type in colname]\n",
    "team_classifier.fit()\n",
    "print(\"K-Fold Mean Accuracy:\", round(team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f002642",
   "metadata": {},
   "source": [
    "#### 2.1.2 Модель с учителем с признаками типа HSV_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d2ca551",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Mean Accuracy: 0.696\n"
     ]
    }
   ],
   "source": [
    "col_type = 'HSV_mean'\n",
    "team_classifier.fit_features_names = [colname for colname in all_fit_feature_names if col_type in colname]\n",
    "team_classifier.fit()\n",
    "print(\"K-Fold Mean Accuracy:\", round(team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce61b326",
   "metadata": {},
   "source": [
    "#### 2.1.3 Модель с учителем с признаками типа RGB_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea44b998",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Mean Accuracy: 0.852\n"
     ]
    }
   ],
   "source": [
    "col_type = 'RGB_hist'\n",
    "team_classifier.fit_features_names = [colname for colname in all_fit_feature_names if col_type in colname]\n",
    "team_classifier.fit()\n",
    "print(\"K-Fold Mean Accuracy:\", round(team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa38d15",
   "metadata": {},
   "source": [
    "#### 2.1.4 Модель с учителем с признаками типа HSV_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cf9806cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Mean Accuracy: 0.85\n"
     ]
    }
   ],
   "source": [
    "col_type = 'HSV_hist'\n",
    "team_classifier.fit_features_names = [colname for colname in all_fit_feature_names if col_type in colname]\n",
    "team_classifier.fit()\n",
    "print(\"K-Fold Mean Accuracy:\", round(team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5556d01",
   "metadata": {},
   "source": [
    "#### 2.1.5 Модель с учителем с признаками RGB_hist и HSV_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ed9537a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Mean Accuracy: 0.85\n"
     ]
    }
   ],
   "source": [
    "col_type1 = 'HSV_hist'\n",
    "col_type2 = 'RGB_hist'\n",
    "\n",
    "team_classifier.fit_features_names = [colname for colname in all_fit_feature_names \n",
    "                                      if col_type1 in colname or col_type1 in col_type2]\n",
    "team_classifier.fit()\n",
    "print(\"K-Fold Mean Accuracy:\", round(team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d378979",
   "metadata": {},
   "source": [
    "#### 2.1.6 Модель с учителем со всеми признаками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2a3a136d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Mean Accuracy: 0.854\n"
     ]
    }
   ],
   "source": [
    "team_classifier.fit_features_names = all_fit_feature_names\n",
    "team_classifier.fit()\n",
    "print(\"K-Fold Mean Accuracy:\", round(team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e70f37",
   "metadata": {},
   "source": [
    "#### 2.1.7 Модель с учителем с удалением признаков на основе анализа корреляции\n",
    "\n",
    "Мы получили лучший результат 0.854 при использовании всех признаков. Теперь попробуем удалить те признаки, которые посчитали ненужными в части анализа корреляции:\n",
    "\n",
    "- удаляем все признаки типа RGB_mean;\n",
    "- в типе признаков RGB_hist оставляем только бины от 0 до 8;\n",
    "- в типе HSV_hist удаляем бины от 9 до 16 для канала V."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "10d87300",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Fold Mean Accuracy: 0.853\n"
     ]
    }
   ],
   "source": [
    "fit_feature_names = [\n",
    " 'HSV_mean_h',\n",
    " 'HSV_mean_s',\n",
    " 'RGB_hist_r_bin_0',\n",
    " 'RGB_hist_r_bin_1',\n",
    " 'RGB_hist_r_bin_2',\n",
    " 'RGB_hist_r_bin_3',\n",
    " 'RGB_hist_r_bin_4',\n",
    " 'RGB_hist_r_bin_5',\n",
    " 'RGB_hist_r_bin_6',\n",
    " 'RGB_hist_r_bin_7',\n",
    " 'RGB_hist_r_bin_8',\n",
    " 'RGB_hist_g_bin_0',\n",
    " 'RGB_hist_g_bin_1',\n",
    " 'RGB_hist_g_bin_2',\n",
    " 'RGB_hist_g_bin_3',\n",
    " 'RGB_hist_g_bin_4',\n",
    " 'RGB_hist_g_bin_5',\n",
    " 'RGB_hist_g_bin_6',\n",
    " 'RGB_hist_g_bin_7',\n",
    " 'RGB_hist_g_bin_8',\n",
    " 'RGB_hist_b_bin_0',\n",
    " 'RGB_hist_b_bin_1',\n",
    " 'RGB_hist_b_bin_2',\n",
    " 'RGB_hist_b_bin_3',\n",
    " 'RGB_hist_b_bin_4',\n",
    " 'RGB_hist_b_bin_5',\n",
    " 'RGB_hist_b_bin_6',\n",
    " 'RGB_hist_b_bin_7',\n",
    " 'RGB_hist_b_bin_8',\n",
    " 'HSV_hist_h_bin_1',\n",
    " 'HSV_hist_h_bin_2',\n",
    " 'HSV_hist_h_bin_3',\n",
    " 'HSV_hist_h_bin_4',\n",
    " 'HSV_hist_h_bin_5',\n",
    " 'HSV_hist_h_bin_6',\n",
    " 'HSV_hist_h_bin_7',\n",
    " 'HSV_hist_h_bin_8',\n",
    " 'HSV_hist_h_bin_9',\n",
    " 'HSV_hist_h_bin_10',\n",
    " 'HSV_hist_h_bin_11',\n",
    " 'HSV_hist_h_bin_12',\n",
    " 'HSV_hist_h_bin_13',\n",
    " 'HSV_hist_h_bin_14',\n",
    " 'HSV_hist_h_bin_15',\n",
    " 'HSV_hist_s_bin_0',\n",
    " 'HSV_hist_s_bin_1',\n",
    " 'HSV_hist_s_bin_2',\n",
    " 'HSV_hist_s_bin_3',\n",
    " 'HSV_hist_s_bin_4',\n",
    " 'HSV_hist_s_bin_5',\n",
    " 'HSV_hist_s_bin_6',\n",
    " 'HSV_hist_s_bin_7',\n",
    " 'HSV_hist_s_bin_8',\n",
    " 'HSV_hist_s_bin_9',\n",
    " 'HSV_hist_s_bin_10',\n",
    " 'HSV_hist_s_bin_11',\n",
    " 'HSV_hist_s_bin_12',\n",
    " 'HSV_hist_s_bin_13',\n",
    " 'HSV_hist_s_bin_14',\n",
    " 'HSV_hist_s_bin_15',\n",
    " 'HSV_hist_v_tbin_0',\n",
    " 'HSV_hist_v_tbin_1',\n",
    " 'HSV_hist_v_tbin_2',\n",
    " 'HSV_hist_v_tbin_3',\n",
    " 'HSV_hist_v_tbin_4',\n",
    " 'HSV_hist_v_tbin_5',\n",
    " 'HSV_hist_v_tbin_6',\n",
    " 'HSV_hist_v_tbin_7',\n",
    " 'HSV_hist_v_tbin_8']\n",
    "\n",
    "team_classifier.fit_features_names = fit_feature_names\n",
    "team_classifier.fit()\n",
    "print(\"K-Fold Mean Accuracy:\", round(team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c39c6a",
   "metadata": {},
   "source": [
    "В результате после удаления признаков мы незначительно ухудшили результат по сравнению с моделью с полным набором признаков (0.853 против 0.854). Отсюда можно сделать вывод, что наши предположения при анализе матриц корреляций были верными."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b0f89b",
   "metadata": {},
   "source": [
    "#### 2.1.8 Модель с учителем с подбором оптимальных признаков через последовательный перебор"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6dad43aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def brute_search_features(classifier, feature_names, fit_predict=False, start_feature_name=None, random_state=SEED):\n",
    "    \"\"\" Последовательный подбор признаков для наилучешго результата метрики accuracy\n",
    "    \n",
    "    Функция последовательно перебирает признаки. Если признак дает прирост по метрике,\n",
    "    то он добавляется к списку лучших. И далее следующий новый признак тестируется\n",
    "    с обновленными списком лучших.\n",
    "    \n",
    "    Параметры\n",
    "    ---------\n",
    "    classifier : TeamClassifier\n",
    "      Экземпляр класса TeamClassifier\n",
    "    feature_names : list\n",
    "      Список наименований признаков\n",
    "    start_feature_name : str\n",
    "      Название признака, с которого будет начат подбор.\n",
    "      Если None, то признак будет выбран случайным образом\n",
    "    fit_predict : bool\n",
    "      Если True, то для обучения модели будет использоваться \n",
    "      метод fit_predict. Использовать только для тех моделей,\n",
    "      у которых нет метода predict (кластеризация).\n",
    "      В этом случае данные не разбиваются на фолды.\n",
    "    random_state : int\n",
    "      Фиксация генератора случайных чисел\n",
    "      \n",
    "    Результат\n",
    "    ---------\n",
    "    best_feature_names, best_accuracy : list, float\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(seed=random_state)\n",
    "    \n",
    "    if start_feature_name is None:\n",
    "        start_feature_name = np.random.choice(feature_names)\n",
    "        \n",
    "    assert start_feature_name in feature_names\n",
    "    \n",
    "    feature_names = np.random.permutation(feature_names)\n",
    "    \n",
    "    best_feature_names = [start_feature_name]\n",
    "    classifier.fit_features_names = best_feature_names\n",
    "    \n",
    "    if fit_predict:\n",
    "        classifier.fit_predict()\n",
    "    else:\n",
    "        classifier.fit()\n",
    "        \n",
    "    best_accuracy = classifier.train_kfold_accuracy\n",
    "    \n",
    "    for new_feature_name in (pbar := tqdm(feature_names)):\n",
    "        \n",
    "        pbar.set_postfix({'best_accuracy': best_accuracy, 'features_len': len(best_feature_names)})\n",
    "        \n",
    "        if new_feature_name == start_feature_name:\n",
    "            continue\n",
    "            \n",
    "        test_feature_names = best_feature_names + [new_feature_name]\n",
    "        \n",
    "        classifier.fit_features_names = test_feature_names\n",
    "        \n",
    "        if fit_predict:\n",
    "            classifier.fit_predict()\n",
    "        else:\n",
    "            classifier.fit()\n",
    "        \n",
    "        test_accuracy = classifier.train_kfold_accuracy\n",
    "        \n",
    "        if test_accuracy > best_accuracy:\n",
    "            best_accuracy = test_accuracy\n",
    "            best_feature_names.append(new_feature_name)\n",
    "    \n",
    "    return best_feature_names, best_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f3ce9993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fc1114a029c4fd99d2bd029d32b6553",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/102 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение accuracy для подбора признаков: 0.87\n",
      "Лучшие признаки:\n",
      "['RGB_hist_b_bin_13',\n",
      " 'RGB_hist_g_bin_8',\n",
      " 'HSV_hist_h_bin_11',\n",
      " 'HSV_hist_h_bin_10',\n",
      " 'RGB_hist_b_bin_15',\n",
      " 'RGB_hist_b_bin_7',\n",
      " 'HSV_hist_v_tbin_7',\n",
      " 'HSV_hist_v_tbin_5',\n",
      " 'RGB_hist_b_bin_9',\n",
      " 'RGB_hist_r_bin_4',\n",
      " 'HSV_mean_h',\n",
      " 'RGB_hist_r_bin_12',\n",
      " 'RGB_hist_g_bin_9',\n",
      " 'HSV_hist_v_tbin_2',\n",
      " 'HSV_hist_s_bin_10',\n",
      " 'RGB_hist_g_bin_11',\n",
      " 'RGB_hist_g_bin_0',\n",
      " 'HSV_hist_s_bin_15',\n",
      " 'RGB_hist_r_bin_5',\n",
      " 'HSV_hist_s_bin_8',\n",
      " 'RGB_hist_g_bin_6',\n",
      " 'HSV_hist_h_bin_8',\n",
      " 'RGB_hist_r_bin_10',\n",
      " 'HSV_hist_h_bin_12',\n",
      " 'RGB_hist_g_bin_12',\n",
      " 'HSV_hist_s_bin_3',\n",
      " 'HSV_hist_v_tbin_8']\n"
     ]
    }
   ],
   "source": [
    "randforest_best_feature_names, randforest_best_accuracy = brute_search_features(team_classifier, all_fit_feature_names)\n",
    "\n",
    "print(\"Значение accuracy для подбора признаков:\", round(randforest_best_accuracy, 3))\n",
    "print(\"Лучшие признаки:\")\n",
    "pprint(randforest_best_feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12eeebe",
   "metadata": {},
   "source": [
    "В результате последовательного подбора мы смогли получить лучший результат accuracy в эксперименте 0.87 и примерно в 4 раза уменьшить количество признаков.\n",
    "\n",
    "### 2.2 Обучение модели без учителя с помощью кластеризации\n",
    "\n",
    "В этом блоке будем тестировать модель **SpectralClustering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7a2e713a",
   "metadata": {},
   "outputs": [],
   "source": [
    "spectral_team_classifier = TeamClassifier.load_from_json(bboxes_json=bboxes_json, image_path=image_path, \n",
    "                                                          model_class=SpectralClustering,\n",
    "                                                          model_params={'n_clusters': 2,\n",
    "                                                                        'affinity': 'nearest_neighbors',\n",
    "                                                                        'n_neighbors': 10,\n",
    "                                                                        'assign_labels': 'discretize',\n",
    "                                                                        'random_state': SEED},\n",
    "                                                          folds=10,\n",
    "                                                          model_type='clustering',\n",
    "                                                          random_state=SEED)\n",
    "\n",
    "all_fit_feature_names = spectral_team_classifier.fit_features_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0430477d",
   "metadata": {},
   "source": [
    "#### 2.2.1 Модель без учителя  с признаками типа RGB_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0e2b02b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.685\n"
     ]
    }
   ],
   "source": [
    "col_type = 'RGB_mean'\n",
    "spectral_team_classifier.fit_features_names = [colname for colname in all_fit_feature_names if col_type in colname]\n",
    "_ = spectral_team_classifier.fit_predict()\n",
    "print(\"Accuracy:\", round(spectral_team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb1a7a4",
   "metadata": {},
   "source": [
    "#### 2.2.2 Модель без учителя  с признаками типа HSV_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd8449fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.596\n"
     ]
    }
   ],
   "source": [
    "col_type = 'HSV_mean'\n",
    "spectral_team_classifier.fit_features_names = [colname for colname in all_fit_feature_names if col_type in colname]\n",
    "_ = spectral_team_classifier.fit_predict()\n",
    "print(\"Accuracy:\", round(spectral_team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c9c500",
   "metadata": {},
   "source": [
    "#### 2.2.3 Модель без учителя  с признаками типа RGB_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c0fdf5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.532\n"
     ]
    }
   ],
   "source": [
    "col_type = 'RGB_hist'\n",
    "spectral_team_classifier.fit_features_names = [colname for colname in all_fit_feature_names if col_type in colname]\n",
    "_ = spectral_team_classifier.fit_predict()\n",
    "print(\"Accuracy:\", round(spectral_team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0085a80f",
   "metadata": {},
   "source": [
    "#### 2.2.4 Модель без учителя  с признаками типа HSV_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fee8adda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.498\n"
     ]
    }
   ],
   "source": [
    "col_type = 'HSV_hist'\n",
    "spectral_team_classifier.fit_features_names = [colname for colname in all_fit_feature_names if col_type in colname]\n",
    "_ = spectral_team_classifier.fit_predict()\n",
    "print(\"Accuracy:\", round(spectral_team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21c5b8f",
   "metadata": {},
   "source": [
    "#### 2.2.5 Модель без учителя  с признаками типа HSV_hist и RGB_hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3906a7c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.498\n"
     ]
    }
   ],
   "source": [
    "col_type1 = 'HSV_hist'\n",
    "col_type2 = 'RGB_hist'\n",
    "\n",
    "spectral_team_classifier.fit_features_names = [colname for colname in all_fit_feature_names \n",
    "                                               if col_type1 in colname or col_type1 in col_type2]\n",
    "_ = spectral_team_classifier.fit_predict()\n",
    "print(\"Accuracy:\", round(spectral_team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0a3981",
   "metadata": {},
   "source": [
    "#### 2.2.6 Модель без учителя со всеми признаками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "14e8e3d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.546\n"
     ]
    }
   ],
   "source": [
    "spectral_team_classifier.fit_features_names = all_fit_feature_names\n",
    "_ = spectral_team_classifier.fit_predict()\n",
    "print(\"Accuracy:\", round(spectral_team_classifier.train_kfold_accuracy, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b90c94",
   "metadata": {},
   "source": [
    "#### 2.2.7 Модель без учителя с подбором оптимальных признаков через последовательный перебор"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7e5b4f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26e8ad287fe44fe7b646105655fc4fe5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/102 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение accuracy для подбора признаков: 0.821\n",
      "Лучшие признаки:\n",
      "['RGB_hist_b_bin_13',\n",
      " 'RGB_hist_g_bin_8',\n",
      " 'HSV_mean_h',\n",
      " 'HSV_hist_v_tbin_2',\n",
      " 'RGB_mean_g',\n",
      " 'RGB_hist_g_bin_0',\n",
      " 'RGB_hist_b_bin_6',\n",
      " 'RGB_hist_r_bin_9',\n",
      " 'RGB_hist_b_bin_2',\n",
      " 'HSV_hist_s_bin_15',\n",
      " 'RGB_mean_b',\n",
      " 'RGB_hist_b_bin_1',\n",
      " 'RGB_hist_r_bin_10',\n",
      " 'RGB_hist_r_bin_7',\n",
      " 'RGB_hist_b_bin_0',\n",
      " 'RGB_hist_r_bin_2',\n",
      " 'HSV_hist_h_bin_3']\n"
     ]
    }
   ],
   "source": [
    "spectral_best_feature_names, spectral_best_accuracy = brute_search_features(spectral_team_classifier, \n",
    "                                                                            all_fit_feature_names,\n",
    "                                                                            fit_predict=True)\n",
    "\n",
    "print(\"Значение accuracy для подбора признаков:\", round(spectral_best_accuracy, 3))\n",
    "print(\"Лучшие признаки:\")\n",
    "pprint(spectral_best_feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df8f511",
   "metadata": {},
   "source": [
    "В результате подбора оптимальных признаков для кластеризации мы смогли значительно улучшить качество модели по оценке accuracy с 0.685 до 0.821.\n",
    "\n",
    "## 3 Поиск оптимальных параметров для лучшей модели с помощью Optuna\n",
    "\n",
    "В нашем эксперименте лучшие результаты показала модель на основе RandomForestClassifier и с линейным подбором оптимальных признаков. Попробуем для этой модели подобрать гиперпараметры с помощью Optuna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "67a3eebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "OPTUNA_BASE_MODEL = TeamClassifier.load_from_json(model_class=SpectralClustering,\n",
    "                                                  bboxes_json=bboxes_json, image_path=image_path, random_state=SEED)\n",
    "BEST_FEATURE_NAMES = randforest_best_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ded5b469",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(trial: optuna.trial.Trial, fit_feature_names=BEST_FEATURE_NAMES) -> float:\n",
    "    \"\"\" Функция оптимизации гиперпараметров модели RandomForestClassifier\n",
    "    \n",
    "    Параметры\n",
    "    ---------\n",
    "    trial : optuna.trial.Trial\n",
    "    \"\"\"\n",
    "    \n",
    "    n_estimators = trial.suggest_int(\"n_estimators\", 10, 3000)\n",
    "    criterion = trial.suggest_categorical('criterion', ['gini', 'entropy'])\n",
    "    max_depth = trial.suggest_int('max_depth', 10, 500)\n",
    "    min_samples_leaf = trial.suggest_int('min_samples_leaf', 1, 20)\n",
    "    min_samples_split = trial.suggest_int('min_samples_split', 2, 50)\n",
    "    max_features = trial.suggest_categorical('max_features', ['auto', 'sqrt', 'log2'])\n",
    "    bootstrap = trial.suggest_categorical('bootstrap', [True, False])\n",
    "    random_state = SEED\n",
    "    \n",
    "    model = OPTUNA_BASE_MODEL\n",
    "    model.fit_features_names = fit_feature_names\n",
    "    model.model_params = {\n",
    "        'n_estimators': n_estimators,\n",
    "        'criterion': criterion,\n",
    "        'max_depth': max_depth,\n",
    "        'min_samples_split': min_samples_split,\n",
    "        'min_samples_leaf': min_samples_leaf,\n",
    "        'max_features': max_features,\n",
    "        'bootstrap': bootstrap,\n",
    "        'random_state': random_state,\n",
    "        'n_jobs': -1,\n",
    "    }\n",
    "    \n",
    "    model.fit()\n",
    "\n",
    "    return model.train_kfold_accuracy\n",
    "\n",
    "def print_best_trial(study):\n",
    "    print(\"Number of finished trials: {}\".format(len(study.trials)))\n",
    "\n",
    "    print(\"Best trial:\")\n",
    "    trial = study.best_trial\n",
    "\n",
    "    print(\"  Number: {}\".format(trial.number))\n",
    "    print(\"  Value: {}\".format(trial.value))\n",
    "\n",
    "    print(\"  Params: \")\n",
    "    for key, value in trial.params.items():\n",
    "        print(\"    {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7b707faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "OPTIMIZE = False\n",
    "\n",
    "if OPTIMIZE:\n",
    "\n",
    "    pruner = optuna.pruners.MedianPruner()\n",
    "\n",
    "    study = optuna.create_study(study_name=\"random_forest_1\", direction=\"maximize\", \n",
    "                                pruner=pruner, load_if_exists=True,\n",
    "                                storage=\"sqlite:///optuna.db\")\n",
    "\n",
    "    if len(study.best_trials) > 0:\n",
    "        print_best_trial(study)\n",
    "\n",
    "    study.enqueue_trial(\n",
    "        {\n",
    "            'n_estimators': 300,\n",
    "            'max_depth': 20,\n",
    "            'criterion': 'gini',\n",
    "            'min_samples_split': 2,\n",
    "            'min_samples_leaf': 1,\n",
    "            'max_features': 'auto',\n",
    "            'bootstrap': True,\n",
    "            'random_state': SEED,\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    study.optimize(objective, n_trials=1000)\n",
    "\n",
    "    print_best_trial(study)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28c4b70",
   "metadata": {},
   "source": [
    "# Итоги"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9aefd8a",
   "metadata": {},
   "source": [
    "В ходе работы мы импользователи 2 типа моделей:\n",
    "- модель с обучением с учителем RandomForestClassifier\n",
    "- модель обучени без учителя SpectralClustering\n",
    "\n",
    "RandomForestClassifier обучали с параметрами:\n",
    "- n_estimators = 300\n",
    "- max_depth = 20\n",
    "\n",
    "SpectralClustering обучали с параметрами:\n",
    "- n_clusters = 2\n",
    "- affinity = nearest_neighbors\n",
    "- n_neighbors = 10\n",
    "- assign_labels = discretize\n",
    "\n",
    "Данные параметры считаем стандартными.\n",
    "\n",
    "Ниже в таблице приведены все результаты экспериментов.\n",
    "\n",
    "| Модель                     | Набор признаков         | Гиперпараметры | Accuracy |\n",
    "| -------------------------- |:-----------------------:|:--------------:| --------:|\n",
    "| RandomForestClassifier     | тип RGB_mean            | Стандартные    | 0.704    |\n",
    "| RandomForestClassifier     | тип HSV_mean            | Стандартные    | 0.696    |\n",
    "| RandomForestClassifier     | тип RGB_hist            | Стандартные    | 0.852    |\n",
    "| RandomForestClassifier     | тип HSV_hist            | Стандартные    | 0.850    |\n",
    "| RandomForestClassifier     | тип RGB_hist и HSV_hist | Стандартные    | 0.850    |\n",
    "| RandomForestClassifier     | все типы                | Стандартные    | 0.854    |\n",
    "| RandomForestClassifier     | линейный подбор типов   | Стандартные    | 0.870    |\n",
    "| RandomForestClassifier     | линейный подбор типов   | Optuna         |**0.874** |\n",
    "| SpectralClustering         | тип RGB_mean            | Стандартные    | 0.685    |\n",
    "| SpectralClustering         | тип HSV_mean            | Стандартные    | 0.408    |\n",
    "| SpectralClustering         | тип RGB_hist            | Стандартные    | 0.520    |\n",
    "| SpectralClustering         | тип HSV_hist            | Стандартные    | 0.495    |\n",
    "| SpectralClustering         | тип RGB_hist и HSV_hist | Стандартные    | 0.495    |\n",
    "| SpectralClustering         | все типы                | Стандартные    | 0.531    |\n",
    "| SpectralClustering         | линейный подбор типов   | Стандартные    | 0.821    |\n",
    "\n",
    "Лучшей оказалась модель Random Forest с линейным подбором типов признаков и подбором гиперпараметров через Optuna.\n",
    "С другой стороны, SpectralClustering с линейным подбором типов признаков тоже показала хороший результат. И ее не требуется переобучивать, если поменяются команды.\n",
    "\n",
    "**Что можно улучшить**:\n",
    "- в разметке есть ошибки разметки классов игроков, если ошибки справить, качество модели может вырасти;\n",
    "- можно попробовать стэкинг разных типов моделей;\n",
    "- можно попробовать использовать нейронные сети;\n",
    "- некоторые боксы игроков сильно перекрываются друг с другом, поэтому данные признаков могут пересекаться, можно попробовать убрать такие пересечения.\n",
    "\n",
    "Следующая ячейка используется для получения результата предсказаний в формате json на тестовых данных с помощью лучшей модели.\n",
    "Запускать необходимо только при наличии таких данных. Предсказания сохраняются в файл test_predict.json."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3b3b537a",
   "metadata": {},
   "outputs": [],
   "source": [
    "PRED_TEST_DATA = False\n",
    "\n",
    "if PRED_TEST_DATA:\n",
    "    \n",
    "    # Укажите путь к разметке в формате json\n",
    "    val_bboxes_json = 'team_classification_test/test_bboxes.json'\n",
    "\n",
    "    # Укажите путь к папке с изображениями\n",
    "    val_image_path = 'team_classification_test/frames'\n",
    "    \n",
    "    fit_features_names = [\n",
    "     'RGB_hist_b_bin_13',\n",
    "     'RGB_hist_g_bin_8',\n",
    "     'HSV_hist_h_bin_11',\n",
    "     'HSV_hist_h_bin_10',\n",
    "     'RGB_hist_b_bin_15',\n",
    "     'RGB_hist_b_bin_7',\n",
    "     'HSV_hist_v_tbin_7',\n",
    "     'HSV_hist_v_tbin_5',\n",
    "     'RGB_hist_b_bin_9',\n",
    "     'RGB_hist_r_bin_4',\n",
    "     'HSV_mean_h',\n",
    "     'RGB_hist_r_bin_12',\n",
    "     'RGB_hist_g_bin_9',\n",
    "     'HSV_hist_v_tbin_2',\n",
    "     'HSV_hist_s_bin_10',\n",
    "     'RGB_hist_g_bin_11',\n",
    "     'RGB_hist_g_bin_0',\n",
    "     'HSV_hist_s_bin_15',\n",
    "     'RGB_hist_r_bin_5',\n",
    "     'HSV_hist_s_bin_8',\n",
    "     'RGB_hist_g_bin_6',\n",
    "     'HSV_hist_h_bin_8',\n",
    "     'RGB_hist_r_bin_10',\n",
    "     'HSV_hist_h_bin_12',\n",
    "     'RGB_hist_g_bin_12',\n",
    "     'HSV_hist_s_bin_3',\n",
    "     'HSV_hist_v_tbin_8']\n",
    "\n",
    "    # Инициализация и обучение модели на исходных данных\n",
    "    final_model = TeamClassifier.load_from_json(bboxes_json=bboxes_json, image_path=image_path, \n",
    "                                                model_class=RandomForestClassifier, fit_features_names=fit_features_names,\n",
    "                                                model_params={'n_estimators': 206, \n",
    "                                                              'max_depth': 42, \n",
    "                                                              'max_features': 'auto',\n",
    "                                                              'min_samples_leaf': 2,\n",
    "                                                              'min_samples_split': 9,\n",
    "                                                              'criterion': 'entropy',\n",
    "                                                              'random_state': SEED,\n",
    "                                                              'n_jobs': -1},\n",
    "                                                folds=10,\n",
    "                                                random_state=SEED)\n",
    "    final_model.fit()\n",
    "    print(\"K-Fold Mean Accuracy:\", round(final_model.train_kfold_accuracy, 4))\n",
    "\n",
    "    json_predict_data = final_model.predict_from_json(bboxes_json=val_bboxes_json, image_path=val_image_path, return_json=True)\n",
    "\n",
    "    with open('test_predict.json', 'w', encoding='utf-8') as f:\n",
    "        f.write(json.dumps(json_predict_data))"
   ]
  }
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